Monitoring the depth of anesthesia using entropy features and an artificial neural network

Shalbaf, Reza; Behnam, Hamid; Sleigh, Jamie; Steyn-Ross, Alistair; Voss, Logan J.

doi:10.1016/j.jneumeth.2013.03.008

Cited by 75 publications

(60 citation statements)

References 30 publications

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“…Various commercial and noncommercial depth of anesthesia monitoring approaches have been developed (Kissin, 2000;Struys et al, 2002;Jordan et al, 2006;Ferenets et al, 2007;Liley et al, 2010;Shalbaf et al, 2013;Shoushtarian et al, 2015b,a) that primarily 5 rely on extraction of features from the EEG to track anesthetic brain state. Despite significant history and recent work attempting to characterise the mulit-channel EEG and brain networks related to anesthesia in more detail (Cimenser et al, 2011;Lewis et al, 2012;Purdon et al, 2013;Kuhlmann et al, 2013;Lee et al, 2013), the international uptake of automated depth of anesthesia monitoring in the clinic is still lagging.…”

Section: Introductionmentioning

confidence: 99%

Neural mass model-based tracking of anesthetic brain states

et al. 2016

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Neural mass model-based tracking of brain states from electroencephalographic signals holds the promise of simultaneously tracking brain states while inferring underlying physiological changes in various neuroscientific and clinical applications. Here, neural mass model-based tracking of brain states using the unscented Kalman filter applied to estimate parameters of the Jansen-Rit cortical population model is evaluated through the application of propofol-based anesthetic state monitoring. In particular, 15 subjects underwent propofol anesthesia induction from awake to anesthetised while behavioural responsiveness was monitored and frontal electroencephalographic signals were recorded.The unscented Kalman filter Jansen-Rit model approach applied to frontal electroencephalography achieved reasonable testing performance for classification of the anesthetic brain state (sensitivity: 0.51; chance sensitivity: 0.17; nearest neighbor sensitivity 0.75) when compared to approaches based on linear (autoregressive moving average) modelling (sensitivity 0.58; nearest neighbor sensitivity: 0.91) and a high performing standard depth of anesthesia monitoring measure, Higuchi Fractal Dimension (sensitivity: 0.50; nearest neighbor sensitivity: 0.88). Moreover, it was found that the unscented Kalman filter based parameter estimates of the inhibitory postsynaptic potential amplitude varied in the physiologically expected direction with increases in propofol concentration, while the estimates of the inhibitory postsynaptic potential rate constant did not. These results combined with analysis of monotonicity of parameter estimates, error analysis of parameter estimates, and observability analysis of the Jansen-Rit model, along with considerations of extensions of the Jansen-Rit model, suggests that the Jansen-Rit model combined with unscented Kalman filtering provides a valuable benckmark for future real-time brain state tracking studies. This is especially true for studies of more complex, but still computationally efficient, neural models of anesthesia that can more accurately track the anesthetic brain state, while simultaneously inferring underlying physiological changes that can potentially provide useful clinical information.

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Section: Introductionmentioning

confidence: 99%

Neural mass model-based tracking of anesthetic brain states

et al. 2016

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“…It has been applied in order to monitor the depth of anaesthesia from EEG signals (Olofsen and Sleigh, 2008;Shalbaf et al, 2013) and has been shown to be a promising tool to reveal abnormalities of cerebral activity in patients with absence epilepsy (Ferlazzo et al, 2014). Like any other measures of entropy, permutation entropy is a convenient measure of regularity, complexity or flattening in the frequency distribution.…”

Section: Non-linear Features 2241 Permutation Entropymentioning

confidence: 99%

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines

Lajnef

Chaibi

Ruby

et al. 2015

Journal of Neuroscience Methods

276

164

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“…For the time lag, it is adequate to use a value of s = 1 to extract most of the information in the EEG (Bandt and Pompe 2002;Li et al 2008). The appropriate value for m is determined six according to other studies (Shalbaf et al 2013b;Li et al 2008) and sampling frequency of EEG signal.…”

Section: Permutation Entropymentioning

confidence: 99%

“…PE is an emerging complexity measure for analyzing non-stationary data. Although PE is computationally efficient, conceptually simple and artifact resistant, it doesn't work at deep anesthetised state, mainly due to high-frequency waves during the suppression period (Shalbaf et al 2013b;Li et al 2008). In this paper, we introduce modified permutation entropy (MPE) index which is robust in the characterization of the burst suppression pattern at high doses of anesthetics.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables

2014

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Monitoring depth of anesthesia (DOA) via vital signs is a major ongoing challenge for anesthetists. A number of electroencephalogram (EEG)-based monitors such as the Bispectral (BIS) index have been proposed. However, anesthesia is related to central and autonomic nervous system functions whereas the EEG signal originates only from the central nervous system. This paper proposes an automated DOA detection system which consists of three steps. Initially, we introduce multiscale modified permutation entropy index which is robust in the characterization of the burst suppression pattern and combine multiscale information. This index quantifies the amount of complexity in EEG data and is computationally efficient, conceptually simple and artifact resistant. Then, autonomic nervous system activity is quantified with heart rate and mean arterial pressure which are easily acquired using routine monitoring machine. Finally, the extracted features are used as input to a linear discriminate analyzer (LDA). The method is validated with data obtained from 25 patients during the cardiac surgery requiring cardiopulmonary bypass. The experimental results indicate that an overall accuracy of 89.4 % can be obtained using combination of EEG measure and hemodynamic variables, together with LDA to classify the vital sign into awake, light, surgical and deep anesthetised states. The results demonstrate that the proposed method can estimate DOA more effectively than the commercial BIS index with a stronger artifact-resistance.

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Monitoring the depth of anesthesia using entropy features and an artificial neural network

Cited by 75 publications

References 30 publications

Neural mass model-based tracking of anesthetic brain states

Neural mass model-based tracking of anesthetic brain states

Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines

Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables

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